Context-Aware Sequential Recommendation

@article{Liu2016ContextAwareSR,
  title={Context-Aware Sequential Recommendation},
  author={Q. Liu and Shu Wu and Diyi Wang and Zhaokang Li and Liang Wang},
  journal={2016 IEEE 16th International Conference on Data Mining (ICDM)},
  year={2016},
  pages={1053-1058}
}
  • Q. Liu, Shu Wu, +2 authors Liang Wang
  • Published 19 September 2016
  • Computer Science
  • 2016 IEEE 16th International Conference on Data Mining (ICDM)
Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has… Expand
Attention-based context-aware sequential recommendation model
TLDR
Experimental results indicate that ACA-GRU outperforms state-of-the-art context-aware models as well as sequence recommendation algorithms, demonstrating the effectiveness of the proposed model. Expand
A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
TLDR
A Hierarchical Contextual Attention-based GRU (HCA-GRU) network that fuse the current hidden state and a contextual hidden state built by the attention mechanism, which leads to a more suitable user's overall interest. Expand
Time Interval Aware Self-Attention for Sequential Recommendation
TLDR
This paper proposes TiSASRec (Time Interval aware Self-attention based sequential recommendation), which models both the absolute positions of items as well as the time intervals between them in a sequence, which outperforms various state-of-the-art sequential models on both sparse and dense datasets and different evaluation metrics. Expand
Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation
TLDR
A network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions and achieves significant improvement compared to state-of-the-art RNN-based methods. Expand
Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks
TLDR
New context-aware sequential recommendation methods, based on Stacked Recurrent Neural Networks, are designed that model the dynamics of contexts and temporal gaps and significantly outperform state-of-the-art context- aware sequential recommender systems. Expand
Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
TLDR
PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Expand
Self-Attentive Sequential Recommendation
TLDR
Extensive empirical studies show that the proposed self-attention based sequential model (SASRec) outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets. Expand
Multi-Behavioral Sequential Prediction with Recurrent Log-Bilinear Model
  • Q. Liu, Shu Wu, Liang Wang
  • Computer Science
  • IEEE Transactions on Knowledge and Data Engineering
  • 2017
TLDR
Considering continuous time difference in behavioral history is a key factor for dynamic prediction, the proposed RLBL model and TA-RLBL model yield significant improvements over the competitive compared methods on three datasets, i.e., Movielens-1M dataset, Global Terrorism Database and Tmall dataset with different numbers of behavior types. Expand
Time-aware Context and Feature Enhancement Sequence Recommendation
  • H. Zhou, Yongan Shu
  • Computer Science
  • 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS)
  • 2020
TLDR
This work proposed Time-aware Context and Feature Enhancement Neural Network (CAPNN) to integrate the time context features and high-order features within users' behavior sequences, which captures the long and short-term static interests of users. Expand
Time is of the Essence: A Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
TLDR
The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return- time predictions over a baseline return-time prediction model. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 14 REFERENCES
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
TLDR
RNN is extended and a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN) is proposed, which can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transitions for different geographical distances. Expand
A Dynamic Recurrent Model for Next Basket Recommendation
TLDR
This work proposes a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN), which not only learns a dynamic representation of a user but also captures global sequential features among baskets. Expand
Contextual Operation for Recommender Systems
  • Shu Wu, Q. Liu, Liang Wang, T. Tan
  • Computer Science
  • IEEE Transactions on Knowledge and Data Engineering
  • 2016
TLDR
Motivated by recent works in natural language processing, the proposed Context Operating Tensor (COT) model represents each context value with a latent vector, and model the contextual information as a semantic operation on the user and item to capture the common semantic effects of contexts. Expand
Factorizing personalized Markov chains for next-basket recommendation
TLDR
This paper introduces an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data and shows that the FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization. Expand
COT: Contextual Operating Tensor for Context-Aware Recommender Systems
TLDR
This work proposes Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector, and generates contextual operating matrix from the contextual operating Tensor and latent vectors of contexts. Expand
Fast context-aware recommendations with factorization machines
TLDR
This work proposes to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions and shows empirically that this approach outperforms Multiverse Recommendation in prediction quality and runtime. Expand
CARS2: Learning Context-aware Representations for Context-aware Recommendations
TLDR
It is shown that the context-aware representations can be learned using an appropriate model that aims to represent the type of interactions between context variables, users and items, and that the CARS2 models achieve competitive recommendation performance, compared to several state-of-the-art approaches. Expand
Learning Hierarchical Representation Model for NextBasket Recommendation
TLDR
This paper introduces a novel recommendation approach, namely hierarchical representation model (HRM), which can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction. Expand
Context-aware recommender systems
TLDR
This chapter argues that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations, and introduces three different algorithmic paradigms for incorporating contextual information into the recommendation process. Expand
TFMAP: optimizing MAP for top-n context-aware recommendation
TLDR
This paper proposes TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context, and presents a fast learning algorithm that exploits several intrinsic properties of average precision to improve the learning efficiency, and to ensure its scalability. Expand
...
1
2
...